A GPU, or graphics processing unit, is a processor built to perform many calculations in parallel. Although originally designed for rendering graphics, this structure is well suited to the matrix and vector operations at the center of deep learning. GPUs accelerate both model training and inference, and multiple units can be connected to handle larger models or datasets. Performance depends not only on raw arithmetic speed but also on memory capacity, memory bandwidth, interconnects, numerical formats, cooling, and software libraries. Consumer, data-center, and integrated GPUs serve different workloads, so the most expensive chip is not automatically the most efficient choice for every AI application.
Micron crossed a $1 trillion market valuation for the first time as booming demand for AI memory chips pushed shares to record highs amid the expanding AI infrastructure race.
AMD will invest more than $10 billion across Taiwan’s semiconductor ecosystem to strengthen AI chip manufacturing and packaging technologies. The move supports deployment of the company’s next-generation Helios AI server platform in 2026.
Google and Blackstone have formed a new AI infrastructure company focused on offering cloud access to Google’s TPU chips. The venture aims to expand alternatives to Nvidia-powered AI infrastructure with $5 billion in initial funding.
Elon Musk said xAI will cease operating as an independent company and become fully integrated into SpaceX under a new SpaceXAI structure. The move combines Musk’s AI models, compute infrastructure, and aerospace operations into a single organization.
Anthropic has signed a compute agreement with SpaceX that adds access to more than 220,000 NVIDIA GPUs at the Colossus 1 data center. The added capacity is already being used to raise Claude usage limits and API availability.
Anthropic has signed a deal to access SpaceXAI’s Colossus 1 supercomputer, adding more than 220,000 NVIDIA GPUs to support Claude training and inference workloads.
OpenAI’s rumored AI-focused smartphone could enter mass production in 2027, earlier than previously expected, according to analyst Ming-Chi Kuo.
Meta will deploy tens of millions of AWS Graviton cores to power its next generation of AI systems. The deal highlights rising demand for CPU-driven infrastructure alongside GPUs.
AI startup Recursive Superintelligence has raised $500 million from Nvidia and GV to pursue self-improving AI systems, despite having no public product.
LinkedIn is testing a new platform that lets users earn up to $150 per hour training AI models. The move taps into fast-growing demand for human feedback in AI.